{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5776be83",
   "metadata": {},
   "source": [
    "### 加载sklearn中的标准数据集\n",
    "\n",
    "#### Iris花 4个特征 3个分类 150个样本 每种50个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ecf2270c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data',\n",
       " 'target',\n",
       " 'frame',\n",
       " 'target_names',\n",
       " 'DESCR',\n",
       " 'feature_names',\n",
       " 'filename']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "list(iris.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6a432f",
   "metadata": {},
   "source": [
    "### 显示数据集信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72e9e2e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcee62a3",
   "metadata": {},
   "source": [
    "### 载入逻辑回归函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08074730",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class LogisticRegression in module sklearn.linear_model._logistic:\n",
      "\n",
      "class LogisticRegression(sklearn.linear_model._base.LinearClassifierMixin, sklearn.linear_model._base.SparseCoefMixin, sklearn.base.BaseEstimator)\n",
      " |  LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)\n",
      " |  \n",
      " |  Logistic Regression (aka logit, MaxEnt) classifier.\n",
      " |  \n",
      " |  In the multiclass case, the training algorithm uses the one-vs-rest (OvR)\n",
      " |  scheme if the 'multi_class' option is set to 'ovr', and uses the\n",
      " |  cross-entropy loss if the 'multi_class' option is set to 'multinomial'.\n",
      " |  (Currently the 'multinomial' option is supported only by the 'lbfgs',\n",
      " |  'sag', 'saga' and 'newton-cg' solvers.)\n",
      " |  \n",
      " |  This class implements regularized logistic regression using the\n",
      " |  'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. **Note\n",
      " |  that regularization is applied by default**. It can handle both dense\n",
      " |  and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit\n",
      " |  floats for optimal performance; any other input format will be converted\n",
      " |  (and copied).\n",
      " |  \n",
      " |  The 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization\n",
      " |  with primal formulation, or no regularization. The 'liblinear' solver\n",
      " |  supports both L1 and L2 regularization, with a dual formulation only for\n",
      " |  the L2 penalty. The Elastic-Net regularization is only supported by the\n",
      " |  'saga' solver.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <logistic_regression>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  penalty : {'l1', 'l2', 'elasticnet', 'none'}, default='l2'\n",
      " |      Used to specify the norm used in the penalization. The 'newton-cg',\n",
      " |      'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is\n",
      " |      only supported by the 'saga' solver. If 'none' (not supported by the\n",
      " |      liblinear solver), no regularization is applied.\n",
      " |  \n",
      " |      .. versionadded:: 0.19\n",
      " |         l1 penalty with SAGA solver (allowing 'multinomial' + L1)\n",
      " |  \n",
      " |  dual : bool, default=False\n",
      " |      Dual or primal formulation. Dual formulation is only implemented for\n",
      " |      l2 penalty with liblinear solver. Prefer dual=False when\n",
      " |      n_samples > n_features.\n",
      " |  \n",
      " |  tol : float, default=1e-4\n",
      " |      Tolerance for stopping criteria.\n",
      " |  \n",
      " |  C : float, default=1.0\n",
      " |      Inverse of regularization strength; must be a positive float.\n",
      " |      Like in support vector machines, smaller values specify stronger\n",
      " |      regularization.\n",
      " |  \n",
      " |  fit_intercept : bool, default=True\n",
      " |      Specifies if a constant (a.k.a. bias or intercept) should be\n",
      " |      added to the decision function.\n",
      " |  \n",
      " |  intercept_scaling : float, default=1\n",
      " |      Useful only when the solver 'liblinear' is used\n",
      " |      and self.fit_intercept is set to True. In this case, x becomes\n",
      " |      [x, self.intercept_scaling],\n",
      " |      i.e. a \"synthetic\" feature with constant value equal to\n",
      " |      intercept_scaling is appended to the instance vector.\n",
      " |      The intercept becomes ``intercept_scaling * synthetic_feature_weight``.\n",
      " |  \n",
      " |      Note! the synthetic feature weight is subject to l1/l2 regularization\n",
      " |      as all other features.\n",
      " |      To lessen the effect of regularization on synthetic feature weight\n",
      " |      (and therefore on the intercept) intercept_scaling has to be increased.\n",
      " |  \n",
      " |  class_weight : dict or 'balanced', default=None\n",
      " |      Weights associated with classes in the form ``{class_label: weight}``.\n",
      " |      If not given, all classes are supposed to have weight one.\n",
      " |  \n",
      " |      The \"balanced\" mode uses the values of y to automatically adjust\n",
      " |      weights inversely proportional to class frequencies in the input data\n",
      " |      as ``n_samples / (n_classes * np.bincount(y))``.\n",
      " |  \n",
      " |      Note that these weights will be multiplied with sample_weight (passed\n",
      " |      through the fit method) if sample_weight is specified.\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *class_weight='balanced'*\n",
      " |  \n",
      " |  random_state : int, RandomState instance, default=None\n",
      " |      Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the\n",
      " |      data. See :term:`Glossary <random_state>` for details.\n",
      " |  \n",
      " |  solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'},             default='lbfgs'\n",
      " |  \n",
      " |      Algorithm to use in the optimization problem.\n",
      " |  \n",
      " |      - For small datasets, 'liblinear' is a good choice, whereas 'sag' and\n",
      " |        'saga' are faster for large ones.\n",
      " |      - For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'\n",
      " |        handle multinomial loss; 'liblinear' is limited to one-versus-rest\n",
      " |        schemes.\n",
      " |      - 'newton-cg', 'lbfgs', 'sag' and 'saga' handle L2 or no penalty\n",
      " |      - 'liblinear' and 'saga' also handle L1 penalty\n",
      " |      - 'saga' also supports 'elasticnet' penalty\n",
      " |      - 'liblinear' does not support setting ``penalty='none'``\n",
      " |  \n",
      " |      Note that 'sag' and 'saga' fast convergence is only guaranteed on\n",
      " |      features with approximately the same scale. You can\n",
      " |      preprocess the data with a scaler from sklearn.preprocessing.\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         Stochastic Average Gradient descent solver.\n",
      " |      .. versionadded:: 0.19\n",
      " |         SAGA solver.\n",
      " |      .. versionchanged:: 0.22\n",
      " |          The default solver changed from 'liblinear' to 'lbfgs' in 0.22.\n",
      " |  \n",
      " |  max_iter : int, default=100\n",
      " |      Maximum number of iterations taken for the solvers to converge.\n",
      " |  \n",
      " |  multi_class : {'auto', 'ovr', 'multinomial'}, default='auto'\n",
      " |      If the option chosen is 'ovr', then a binary problem is fit for each\n",
      " |      label. For 'multinomial' the loss minimised is the multinomial loss fit\n",
      " |      across the entire probability distribution, *even when the data is\n",
      " |      binary*. 'multinomial' is unavailable when solver='liblinear'.\n",
      " |      'auto' selects 'ovr' if the data is binary, or if solver='liblinear',\n",
      " |      and otherwise selects 'multinomial'.\n",
      " |  \n",
      " |      .. versionadded:: 0.18\n",
      " |         Stochastic Average Gradient descent solver for 'multinomial' case.\n",
      " |      .. versionchanged:: 0.22\n",
      " |          Default changed from 'ovr' to 'auto' in 0.22.\n",
      " |  \n",
      " |  verbose : int, default=0\n",
      " |      For the liblinear and lbfgs solvers set verbose to any positive\n",
      " |      number for verbosity.\n",
      " |  \n",
      " |  warm_start : bool, default=False\n",
      " |      When set to True, reuse the solution of the previous call to fit as\n",
      " |      initialization, otherwise, just erase the previous solution.\n",
      " |      Useless for liblinear solver. See :term:`the Glossary <warm_start>`.\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.\n",
      " |  \n",
      " |  n_jobs : int, default=None\n",
      " |      Number of CPU cores used when parallelizing over classes if\n",
      " |      multi_class='ovr'\". This parameter is ignored when the ``solver`` is\n",
      " |      set to 'liblinear' regardless of whether 'multi_class' is specified or\n",
      " |      not. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n",
      " |      context. ``-1`` means using all processors.\n",
      " |      See :term:`Glossary <n_jobs>` for more details.\n",
      " |  \n",
      " |  l1_ratio : float, default=None\n",
      " |      The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only\n",
      " |      used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent\n",
      " |      to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent\n",
      " |      to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a\n",
      " |      combination of L1 and L2.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  \n",
      " |  classes_ : ndarray of shape (n_classes, )\n",
      " |      A list of class labels known to the classifier.\n",
      " |  \n",
      " |  coef_ : ndarray of shape (1, n_features) or (n_classes, n_features)\n",
      " |      Coefficient of the features in the decision function.\n",
      " |  \n",
      " |      `coef_` is of shape (1, n_features) when the given problem is binary.\n",
      " |      In particular, when `multi_class='multinomial'`, `coef_` corresponds\n",
      " |      to outcome 1 (True) and `-coef_` corresponds to outcome 0 (False).\n",
      " |  \n",
      " |  intercept_ : ndarray of shape (1,) or (n_classes,)\n",
      " |      Intercept (a.k.a. bias) added to the decision function.\n",
      " |  \n",
      " |      If `fit_intercept` is set to False, the intercept is set to zero.\n",
      " |      `intercept_` is of shape (1,) when the given problem is binary.\n",
      " |      In particular, when `multi_class='multinomial'`, `intercept_`\n",
      " |      corresponds to outcome 1 (True) and `-intercept_` corresponds to\n",
      " |      outcome 0 (False).\n",
      " |  \n",
      " |  n_iter_ : ndarray of shape (n_classes,) or (1, )\n",
      " |      Actual number of iterations for all classes. If binary or multinomial,\n",
      " |      it returns only 1 element. For liblinear solver, only the maximum\n",
      " |      number of iteration across all classes is given.\n",
      " |  \n",
      " |      .. versionchanged:: 0.20\n",
      " |  \n",
      " |          In SciPy <= 1.0.0 the number of lbfgs iterations may exceed\n",
      " |          ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  SGDClassifier : Incrementally trained logistic regression (when given\n",
      " |      the parameter ``loss=\"log\"``).\n",
      " |  LogisticRegressionCV : Logistic regression with built-in cross validation.\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  The underlying C implementation uses a random number generator to\n",
      " |  select features when fitting the model. It is thus not uncommon,\n",
      " |  to have slightly different results for the same input data. If\n",
      " |  that happens, try with a smaller tol parameter.\n",
      " |  \n",
      " |  Predict output may not match that of standalone liblinear in certain\n",
      " |  cases. See :ref:`differences from liblinear <liblinear_differences>`\n",
      " |  in the narrative documentation.\n",
      " |  \n",
      " |  References\n",
      " |  ----------\n",
      " |  \n",
      " |  L-BFGS-B -- Software for Large-scale Bound-constrained Optimization\n",
      " |      Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales.\n",
      " |      http://users.iems.northwestern.edu/~nocedal/lbfgsb.html\n",
      " |  \n",
      " |  LIBLINEAR -- A Library for Large Linear Classification\n",
      " |      https://www.csie.ntu.edu.tw/~cjlin/liblinear/\n",
      " |  \n",
      " |  SAG -- Mark Schmidt, Nicolas Le Roux, and Francis Bach\n",
      " |      Minimizing Finite Sums with the Stochastic Average Gradient\n",
      " |      https://hal.inria.fr/hal-00860051/document\n",
      " |  \n",
      " |  SAGA -- Defazio, A., Bach F. & Lacoste-Julien S. (2014).\n",
      " |      SAGA: A Fast Incremental Gradient Method With Support\n",
      " |      for Non-Strongly Convex Composite Objectives\n",
      " |      https://arxiv.org/abs/1407.0202\n",
      " |  \n",
      " |  Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin (2011). Dual coordinate descent\n",
      " |      methods for logistic regression and maximum entropy models.\n",
      " |      Machine Learning 85(1-2):41-75.\n",
      " |      https://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from sklearn.datasets import load_iris\n",
      " |  >>> from sklearn.linear_model import LogisticRegression\n",
      " |  >>> X, y = load_iris(return_X_y=True)\n",
      " |  >>> clf = LogisticRegression(random_state=0).fit(X, y)\n",
      " |  >>> clf.predict(X[:2, :])\n",
      " |  array([0, 0])\n",
      " |  >>> clf.predict_proba(X[:2, :])\n",
      " |  array([[9.8...e-01, 1.8...e-02, 1.4...e-08],\n",
      " |         [9.7...e-01, 2.8...e-02, ...e-08]])\n",
      " |  >>> clf.score(X, y)\n",
      " |  0.97...\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      LogisticRegression\n",
      " |      sklearn.linear_model._base.LinearClassifierMixin\n",
      " |      sklearn.base.ClassifierMixin\n",
      " |      sklearn.linear_model._base.SparseCoefMixin\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  fit(self, X, y, sample_weight=None)\n",
      " |      Fit the model according to the given training data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          Training vector, where n_samples is the number of samples and\n",
      " |          n_features is the number of features.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,)\n",
      " |          Target vector relative to X.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,) default=None\n",
      " |          Array of weights that are assigned to individual samples.\n",
      " |          If not provided, then each sample is given unit weight.\n",
      " |      \n",
      " |          .. versionadded:: 0.17\n",
      " |             *sample_weight* support to LogisticRegression.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |          Fitted estimator.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The SAGA solver supports both float64 and float32 bit arrays.\n",
      " |  \n",
      " |  predict_log_proba(self, X)\n",
      " |      Predict logarithm of probability estimates.\n",
      " |      \n",
      " |      The returned estimates for all classes are ordered by the\n",
      " |      label of classes.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Vector to be scored, where `n_samples` is the number of samples and\n",
      " |          `n_features` is the number of features.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      T : array-like of shape (n_samples, n_classes)\n",
      " |          Returns the log-probability of the sample for each class in the\n",
      " |          model, where classes are ordered as they are in ``self.classes_``.\n",
      " |  \n",
      " |  predict_proba(self, X)\n",
      " |      Probability estimates.\n",
      " |      \n",
      " |      The returned estimates for all classes are ordered by the\n",
      " |      label of classes.\n",
      " |      \n",
      " |      For a multi_class problem, if multi_class is set to be \"multinomial\"\n",
      " |      the softmax function is used to find the predicted probability of\n",
      " |      each class.\n",
      " |      Else use a one-vs-rest approach, i.e calculate the probability\n",
      " |      of each class assuming it to be positive using the logistic function.\n",
      " |      and normalize these values across all the classes.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Vector to be scored, where `n_samples` is the number of samples and\n",
      " |          `n_features` is the number of features.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      T : array-like of shape (n_samples, n_classes)\n",
      " |          Returns the probability of the sample for each class in the model,\n",
      " |          where classes are ordered as they are in ``self.classes_``.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.linear_model._base.LinearClassifierMixin:\n",
      " |  \n",
      " |  decision_function(self, X)\n",
      " |      Predict confidence scores for samples.\n",
      " |      \n",
      " |      The confidence score for a sample is proportional to the signed\n",
      " |      distance of that sample to the hyperplane.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix, shape (n_samples, n_features)\n",
      " |          Samples.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)\n",
      " |          Confidence scores per (sample, class) combination. In the binary\n",
      " |          case, confidence score for self.classes_[1] where >0 means this\n",
      " |          class would be predicted.\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Predict class labels for samples in X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix, shape (n_samples, n_features)\n",
      " |          Samples.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      C : array, shape [n_samples]\n",
      " |          Predicted class label per sample.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the mean accuracy on the given test data and labels.\n",
      " |      \n",
      " |      In multi-label classification, this is the subset accuracy\n",
      " |      which is a harsh metric since you require for each sample that\n",
      " |      each label set be correctly predicted.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True labels for `X`.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Mean accuracy of ``self.predict(X)`` wrt. `y`.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.linear_model._base.SparseCoefMixin:\n",
      " |  \n",
      " |  densify(self)\n",
      " |      Convert coefficient matrix to dense array format.\n",
      " |      \n",
      " |      Converts the ``coef_`` member (back) to a numpy.ndarray. This is the\n",
      " |      default format of ``coef_`` and is required for fitting, so calling\n",
      " |      this method is only required on models that have previously been\n",
      " |      sparsified; otherwise, it is a no-op.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |          Fitted estimator.\n",
      " |  \n",
      " |  sparsify(self)\n",
      " |      Convert coefficient matrix to sparse format.\n",
      " |      \n",
      " |      Converts the ``coef_`` member to a scipy.sparse matrix, which for\n",
      " |      L1-regularized models can be much more memory- and storage-efficient\n",
      " |      than the usual numpy.ndarray representation.\n",
      " |      \n",
      " |      The ``intercept_`` member is not converted.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |          Fitted estimator.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      For non-sparse models, i.e. when there are not many zeros in ``coef_``,\n",
      " |      this may actually *increase* memory usage, so use this method with\n",
      " |      care. A rule of thumb is that the number of zero elements, which can\n",
      " |      be computed with ``(coef_ == 0).sum()``, must be more than 50% for this\n",
      " |      to provide significant benefits.\n",
      " |      \n",
      " |      After calling this method, further fitting with the partial_fit\n",
      " |      method (if any) will not work until you call densify.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : dict\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
      " |      parameters of the form ``<component>__<parameter>`` so that it's\n",
      " |      possible to update each component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : estimator instance\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "help(LogisticRegression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8492b20",
   "metadata": {},
   "source": [
    "### 执行逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d43b660a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100000.0, multi_class='multinomial')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因为是学习 所以只取2个维度 好画图\n",
    "X = iris.data[:, :2]\n",
    "y = iris.target\n",
    "\n",
    "logreg = LogisticRegression(penalty='l2', C=1e5, multi_class='multinomial')\n",
    "logreg.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2966314",
   "metadata": {},
   "source": [
    "### 查看执行结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5348837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "x0, x1 = np.meshgrid(\n",
    "    np.linspace(4, 8, 500).reshape(-1, 1),\n",
    "    np.linspace(1, 5, 200).reshape(-1, 1),\n",
    ")\n",
    "X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "\n",
    "y_proba = logreg.predict_proba(X_new)\n",
    "y_predict = logreg.predict(X_new)\n",
    "\n",
    "zz1 = y_proba[:, 1].reshape(x0.shape)\n",
    "zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(X[y == 2, 0], X[y == 2, 1], \"g^\", label=\"Iris virginica\")\n",
    "plt.plot(X[y == 1, 0], X[y == 1, 1], \"bs\", label=\"Iris versicolor\")\n",
    "plt.plot(X[y == 0, 0], X[y == 0, 1], \"yo\", label=\"Iris setosa\")\n",
    "\n",
    "custom_cmap = ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n",
    "\n",
    "plt.contourf(x0, x1, zz, cmap=custom_cmap)\n",
    "contour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg)\n",
    "plt.clabel(contour, inline=1, fontsize=12)\n",
    "\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"center left\", fontsize=14)\n",
    "plt.axis([4, 8, 1, 5])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec0f21c",
   "metadata": {},
   "source": [
    "### 逻辑回归：一维 两类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d1cf3224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = iris[\"data\"][:, 3:]  # 一维:petal width \n",
    "y = (iris[\"target\"] == 2).astype(np.int32)  # 两类:1 if Iris virginica, else 0\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression(solver=\"lbfgs\", random_state=42)\n",
    "log_reg.fit(X, y)\n",
    "\n",
    "X_new = np.linspace(0, 3, 1000).reshape(-1, 1)\n",
    "y_proba = log_reg.predict_proba(X_new)\n",
    "\n",
    "plt.plot(X_new, y_proba[:, 1], \"g-\", linewidth=2, label=\"Iris virginica\")\n",
    "plt.plot(X_new, y_proba[:, 0], \"b--\", linewidth=2, label=\"Not Iris virginica\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2def37e3",
   "metadata": {},
   "source": [
    "### 逻辑回归：二维 二类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2fa2d467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.int32)\n",
    "\n",
    "log_reg = LogisticRegression(solver=\"lbfgs\", C=10**10, random_state=42)\n",
    "log_reg.fit(X, y)\n",
    "\n",
    "x0, x1 = np.meshgrid(\n",
    "        np.linspace(2.9, 7, 500).reshape(-1, 1),\n",
    "        np.linspace(0.8, 2.7, 200).reshape(-1, 1),\n",
    "    )\n",
    "X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "y_proba = log_reg.predict_proba(X_new)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(X[y==0, 0], X[y==0, 1], \"bs\")\n",
    "plt.plot(X[y==1, 0], X[y==1, 1], \"g^\")\n",
    "\n",
    "zz = y_proba[:, 1].reshape(x0.shape)\n",
    "contour = plt.contour(x0, x1, zz, cmap=plt.cm.brg)\n",
    "plt.clabel(contour, inline=1, fontsize=12)\n",
    "\n",
    "left_right = np.array([2.9, 7])\n",
    "boundary = -(log_reg.coef_[0][0] * left_right + log_reg.intercept_[0]) / log_reg.coef_[0][1]\n",
    "plt.plot(left_right, boundary, \"k--\", linewidth=3)\n",
    "\n",
    "\n",
    "plt.text(3.5, 1.5, \"Not Iris virginica\", fontsize=14, color=\"b\", ha=\"center\")\n",
    "plt.text(6.5, 2.3, \"Iris virginica\", fontsize=14, color=\"g\", ha=\"center\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.axis([2.9, 7, 0.8, 2.7])\n",
    "\n",
    "plt.show()"
   ]
  }
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